Method and system for creating and using barcodes

ABSTRACT

Methods for efficiently retrieving information from an image of a symbol are described. Symbols are described that contain detection patterns that facilitate the determination of location, alignment, size and orientation of the symbol in an image. Detection patterns are described that possess geometric shapes susceptible to efficient decoding using probabilistic detection algorithms. Detection patterns are described that are provided in colors, shapes and sizes different from the color, shape and sizes of modules carrying information in the symbol. Methods are described for identifying the location and size of detection patterns in images of the symbol and for locating modules in the symbol to facilitate extraction of information carried by the modules.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/612,447 by Han Kiliccote, which was filed on Dec. 18, 2006, entitled“Method and System for Creating and Using Barcodes,” which will beissued as U.S. Pat. No. 7,571,864 and which claimed the benefit ofpriority from U.S. Provisional Patent Application No. 60/751,035 to HanKiliccote, which was filed on Dec. 16, 2005 and entitled “Method andSystem for Creating and Using Barcodes,” which applications are herebyfully incorporated herein by reference for all purposes.

This application is also related to U.S. non-provisional patentapplication Ser. No. 11/357,369 to Han Kiliccote, which was filed onFeb. 16, 2006 and entitled “Method of Transferring Data through MovingImages,” which issued as U.S. Pat. No. 7,543,748 and which is herebyfully incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to barcodes. More particularly,the present invention relates to methods and systems for creatingbarcodes where finder position detection and alignment patterns includecertain geometric shapes that facilitate detection and decoding.

2. Description of Related Art

Typically, barcodes are machine-readable representation of informationdisplayed in a visual format on a surface. There are different types ofbarcodes. Linear barcodes store data in the widths and spacing ofprinted parallel lines. Stacked barcodes and 2-dimensional (2D)barcodes, which represent stored data in patterns of dots, concentriccircles and hidden images, have a higher data storage capacity thanlinear barcodes. Barcodes may be read by optical scanners called barcodereaders and/or scanned from an image by special software.

To increase the amount of information that can be stored in a givenspace, linear requirements of simple barcodes have been extended withmatrix codes. Matrix codes are a type of 2D barcode. Matrix codes aremade of a grid of square cells called modules. Stacked barcodes aresimilar to 2D barcodes. Stacked barcodes are formed by taking atraditional linear barcode and placing it in an envelope that allowsmultiple rows of linear barcodes. FIG. 1 illustrates an example of amatrix barcode called QRCode¹ typically used in the art today. ¹ QRCodeis trademark of Denso-Wave, Inc.

The mapping between data and the barcode that embeds the data is calleda symbology. Symbologies include specifications relating to variousparameters, such as, the encoding of the data, the start and stopmarkers into bars or dots and spaces, the size of the quiet zonerequired to be before and after the barcode. Symbologies also include aspecification for forward error correction used in the barcode.

Linear and 2D barcode symbologies may use horizontal and vertical timingpatterns to facilitate decoding of the barcode. The timing patternsusually include a one-module wide row or column of alternating dark andlight modules, commencing and ending with a dark module. Linear and 2Dbarcode symbologies may use position detection patterns that enable thesymbol density and version to be determined and may provide datumpositions for determining module coordinates.

Existing symbologies encode symbols in a linear or 2D image. Morespecifically, such symbologies use black and white bars or dots thatoverall define the barcode image. When decoding a linear or 2D codeexample, various confusing cases may arise. For example, in one case,consecutive long sequences of whites (or blacks) may render it hard todetermine the number of consecutive whites and blacks due to cameradistortion and camera viewing angle. FIG. 2 illustrates a typicaldifference between module sizes due to certain perspective distortions,such as parallax.

In another example, the typical barcode may utilize various patterns todetermine the orientation and location of the image. These patternsmight repeat themselves in the data. Such repetition might renderdecoding the barcode a difficult, if not impossible task.

In a further example, due to the large possible orientations of thecamera relative to the barcode, each symbol may take on a different sizeand/or shape relative to the scanner image. The symbologies may assign atiming pattern in the image and try to resolve the location of eachsymbol through the timing and location patterns. At large or highlyskewed viewing angles, this approach might not work, because the symbolsmay scale up to distorted and different-sized shapes at the final imagethat are unrecognizable (i.e., un-decodable) to the scanner software.

In barcode symbologies, alignment patterns are typically used to correcterrors in the estimates. Certain image distortions, such as perspectivedistortions, defects in the lenses of the camera, defects on thesurfaces or natural curvatures of certain displays such as CRT monitors,may cause very large distortions. If the error caused by distortioncauses the decoder to lose where the alignment pattern is, either thealignment pattern must be searched around where the estimated position,which may cause significant performance problems or it would render thebarcode undecipherable.

Hence, it would be desirable to provide a method and system that iscapable of creating barcodes that facilitate decoding in a manner thatsolves one or more of the problems with the typical barcodes of today.

SUMMARY

Certain embodiments of the invention provide a matrix symbology andmethods for encoding and decoding symbols encoded according to thematrix symbology. In certain embodiments, a symbol is encoded or decodedas an array of modules carrying information and arranged in an overallgeometric pattern, or barcode symbol. According to certain aspects ofthe invention, a plurality of additional patterns is encoded in thesymbol to assist decoding of images of the symbol. The additionalpatterns provide information that facilitates the correction of imagesof the symbol that are misaligned, disoriented or distorted. Accordingto certain aspects of the invention, these additional patterns can beprovided in colors, shapes and sizes different from the modules suchthat the additional patterns are more easily distinguished from themodules and to improve efficiency of detection of the additionalpatterns.

In certain embodiments, a symbol may include a finder pattern located inrelation to or centered on a specific point in the pattern such as thecenter or corners of the symbol. The symbol can include positiondetection patterns, orientation patterns and location detection patternsthat allow a decoding algorithm to further verify existence of thesymbol and detect the orientation, alignment and position of the symboland components of the symbol in relation to one another.

Certain embodiments provide methods for decoding symbols providedaccording to aspects of the invention. In certain embodiments of theinvention, symbol decoding speed can be increased through the use ofgeometric shapes for position detection patterns, orientation patternsand location detection patterns. Methods of efficiently detecting suchgeometric shapes are described in which probabilistic detectionalgorithms operate to determine whether a starting point is locatedinside a specific geometric shape.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Aspects and features of the present invention will become apparent tothose ordinarily skilled in the art from the following detaileddescription of embodiments of the invention in conjunction with theaccompanying drawings, wherein:

FIG. 1 illustrates an example of a stacked barcode called QRCodetypically used in the art today;

FIG. 2 illustrates a typical difference between module sizes due tocertain perspective distortions, such as parallax;

Color FIGS. 3-5 illustrate a schematic diagrams showing a color codedimage generated according to certain embodiments of the presentinvention;

FIG. 6 illustrates a grayscale printout of the color coded imagegenerated according to certain embodiments of the present invention;

FIG. 7 illustrates a black-and-white printout of the color coded imagegenerated according to certain embodiments of the present invention;

FIG. 8 illustrates a black-and-white dithered printout of the colorcoded image generated according to certain embodiments of the presentinvention;

Color FIG. 9 illustrates the exemplary eight scan lines separated by a45 degree angle that can be used to detect a circle according to certainembodiments of the present invention;

Color FIG. 10 illustrates the exemplary eight segments formed byconstraining the scan lines to the edge of the circle as used in certainembodiments of the present invention;

FIG. 11 illustrates the exemplary 12 points that can be used to detect acircle larger than ⅓ of the size of image according to certainembodiments of the present invention;

FIG. 12 illustrates the exemplary 48 points that can used to detect acircle larger than ⅙ of the size of image according to certainembodiments of the present invention;

FIG. 13 illustrates the exemplary points that can be used to detect acircle larger than 1/12 of the size of image according to certainembodiments of the present invention;

FIG. 14 illustrates the exemplary points that can be used to detect acircle larger than 1/24 of the size of image according to certainembodiments of the present invention;

Color FIG. 15 illustrates an area to be searched and discarded for dataposition patterns generated according to certain embodiments of thepresent invention;

Color FIG. 16 illustrates an example of an image, where the size of theposition and alignment patterns can be derived from other patternswithin the image, generated according to certain embodiments of thepresent invention;

Color FIG. 17 illustrates exemplary error correction of an imagegenerated according to certain embodiments of the present invention;

Color FIG. 18 illustrates exemplary error correction using shapedetection for smaller errors of smaller shapes of an image generatedaccording to certain embodiments of the present invention;

Color FIG. 19 illustrates timing patterns in QRCode;

Color FIG. 20 illustrates timing patterns in DataMatrix² code; ² DataMatrix is a trademark of International Data Matrix, Inc.

Color FIG. 21 illustrates how two alignment patterns can be used toderive the position of the modules according to certain embodiments ofthe present invention; and

Color FIG. 22 illustrates an exemplary geometric calculation of howmodule location can be derived according to certain embodiments of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will now be described in detail with reference tothe drawings, which are provided as illustrative examples of theinvention so as to enable those skilled in the art to practice theinvention. Notably, the figures and examples below are not meant tolimit the scope of the present invention. Where certain elements of thepresent invention can be partially or fully implemented using knowncomponents, only those portions of such known components that arenecessary for an understanding of the present invention will bedescribed, and detailed descriptions of other portions of such knowncomponents will be omitted so as not to obscure the invention. Further,the present invention encompasses present and future known equivalentsto the components referred to herein by way of illustration.

In certain embodiments, a matrix symbology includes an array of modulesarranged in an overall geometric pattern, or barcode symbol, such as arectangle. According to certain aspects of the invention, a plurality ofadditional patterns may be encoded in the symbol to assist decoding ofimages of the symbol, including images that are misaligned or distorted.According to certain aspects of the invention, these additional patternscan be provided in colors, shapes and sizes different from the modulessuch that the additional patterns are more easily distinguished from themodules and to improve efficiency of detection of the additionalpatterns.

The overall geometric pattern may include a finder pattern located inrelation to, or centered on a specific point in the pattern such as thecenter or corners of the symbol. Each of the schematic diagrams in FIGS.3-5 is a color coded image generated of a barcode or symbol 30 accordingto certain embodiments of the present invention. The specific examplesin FIGS. 3-5 show the finder pattern 31 at the center of the barcode orsymbol 30; however this aspect is not meant to limit the scope certainembodiments of the present invention. The finder pattern 31 can beselected in such a way as to facilitate determination of the position,size and inclination of the barcode or symbol 30.

The overall geometric pattern 30 may include position detection patterns32 that allow a decoding algorithm to further verify that symbol 30exists and to detect the orientation of the barcode or symbol 30.Detection of orientation may be facilitated by locating the position ofone or more orientation detection patterns 33 provided in apredetermined configuration with barcode or symbol 30. The positiondetection patterns 32 can be selected and encoded so that similarpatterns have a low probability of being encountered in, or in thevicinity of, the barcode, or in pictures where the barcode or symbol 30is absent. This selection and encoding can also enable rapididentification of a possible barcode in the field of view of thedetection device.

The overall geometric pattern may also include alignment patterns 34that allow the decoding algorithm to further adjust the location of thebarcode or symbol 30. In certain embodiments of the invention, a schemeinvolving color encoding of the finder pattern 31 may be used. The usageof color for finder patterns 31 can further increase the reliability andspeed of decoding the barcode or symbol 30.

A color encoding scheme according to certain embodiments of theinvention can be implemented as follows. Data encoded in the barcode cantypically be considered a set of binary bits where each bit can be 0or 1. Color encoding can be implemented such that the bits forming thedata have a large separation in color space. For example, 0 can beassigned to be a black symbol and 1 can be a white symbol. Other colorvariations can be employed by, for example, substituting black symbolswith a darkly colored symbol (e.g., blue, red or green) and substitutingwhite symbol with a lightly colored symbol (e.g., white, yellow, lightblue). In certain embodiments, finder 31, position detection 32 andalignment 34 patterns can use dark colors that are significantlydifferently from colors used for data symbols. For example, if a barcodeor symbol 30 encodes data symbols using black or white, then the finderpattern 31 may be provided in blue and white, position detectionpatterns 32 may be provided in green and white and alignment pattern 34may be provided in red and white. An example of this is shown in FIG. 3.According to certain aspects of the invention, the usage of differentcolors for different types of patterns can increase the overallreliability and speed of decoding the symbol for certain symbologies.However, depending on the properties of the particular symbology, theremay not be a significant benefit to using different colors for differenttypes of patterns.

When a multicolored symbol is imaged in gray scale, or printed on ablack and white printer, shapes of patterns can still be identified andthe barcode can still be decoded. An example of the barcode of FIG. 4 insimulated gray-scale is shown in FIG. 6. An example of the barcode ofFIG. 4 in simulated black-and-white printout is shown in FIG. 7. Anexample of the barcode of FIG. 4 in Floyd-Steinberg ditheredblack-and-white printout is shown in FIG. 8.

In certain embodiments of the invention, finder patterns 31 are usedthat can be detected by only inspecting a subset of pixels captured by ascanning device. Rather than inspecting most of the pixels in thecaptured image, the algorithm described herein can find one or morefinder patterns 31 by inspecting a minority of the pixels associatedwith the captured image. This facilitates increased decoding speed ofthe associated barcode or symbol 30. In certain embodiments, the numberof pixels inspected by the algorithm can be several orders of magnitudeless than what can be achieved in other barcode symbologies.

In an image that contains a barcode or symbol 30, the location and thesize of the barcode or symbol 30 is usually not a priori known to thescanning device. The decoding algorithm must first identify the locationof the barcode or symbol 30. This can be accomplished by first locatingthe one or more finder patterns 31 in the image. One finder patterndetection algorithm that could be used by symbologies looks for thefinder pattern 31 in an iterative fashion starting from one corner ofthe barcode or symbol 30 and then inspects all pixels until all of thepixels have been searched.

In certain embodiments of the invention, symbol decoding speed can beincreased through the use of geometric shapes and point detectionalgorithms. The use of geometric shapes allows probabilisticverification of whether a point is inside a specific geometric shape.Certain geometric shapes allow easier detection than the others. Forexample, a circle can be efficiently detected with probabilisticmethods. A circle can be detected with a good probability using a scanline method. With reference to FIG. 9, an algorithm for probabilisticdetection may work as follows. Given a starting point P_(start) 900,multiple scan lines 901-908 can be drawn from that starting point 900with specific angle increments. FIG. 9 illustrates the exemplary eightscan lines 901-908 separated by a 45 degree angle that can be used todetect a circle according to certain embodiments of the presentinvention. As shown in FIG. 9, the scan lines 901-908 are annotated asL_(i), each emanating from the starting point P_(start) 900, where i isan angular measurement in degrees. Those skilled in the art willrecognize that other angular displacements, number of lines and startingpoint can also be employed by the present invention.

Starting from P_(start) 900, points along the scan lines can beinspected to see if there is a significant change in the intensity levelcompared to P_(start) 900. When a significant change in the intensitylevel is detected, the point at which that intensity change occurred canbe assumed to be at the boundary of the shape. Points along all scanlines 901-908 can be examined, but in some embodiments, sufficientinformation to enable identification of a shape or artifact may beobtained from an examination of fewer than all scan lines 901-908. Afterinspection of a portion or all scan lines 901-908, further examinationof the scan lines 901-908 can be restricted to segments within estimatedboundaries of the shape, as shown in FIG. 10 or segments within theestimated boundaries of additional scan lines can be examined.

The segments can then be used to detect the shape (in this example, acircle). The segments in 45 degree angular increments can be defined bythe points [P(−1, −1), P_(start)], [P(−1, 0), P_(start)], [P(−1, 1),P_(start)], [P(0, −1), P_(start)], [P(0, 1), P_(start)], [P(1, −1),P_(start)], [P(1, 0), P_(start)] and [P(1, 1), P_(start)]. In thisexample, the center of the circle 900 can be calculated as P(x, y) wherex is the horizontal position of P(−1, 0) added to the horizontalposition of P(1, 0) divided by 2 and y is the vertical position of P(0,−1) added to the vertical position of P(0, 1) divided by 2. The radiusof the circle can be calculated as the average distance from the centerpoint to the positions of these points. Then, probabilistically a circlewould exist if the points P(−1, −1), P(−1, 1), P(1, −1) and P(1, 1)would also form a circle with the same center close by to the circle andwith a similar radius. A user can define tolerances and otherprobabilistic measurement representing a level of closeness that permitsa shape detection algorithm to determine whether the shape is a circle.In the example, eight scan lines are used, but more scan lines can beused to increase probabilistic accuracy and to decrease probabilisticfalse positives (e.g., detecting a circle where none exists). Howevermore scan lines can have a negative impact on performance of shapedetection. Other shape detection algorithms and methods can beimplemented to facilitate detection of other geometric shapes foroperation in “noisy” environments, for optimization of performance ofcomputing systems and for other reasons. In the example described, areference point (center of circle) and a measure of size (radius) of ageometric shape (circle) can describe the geometric shape as presentedin the symbol or barcode and can be used to correlate a set of boundarypoints identified in the image to the geometric shape. However, othershape defining characteristics can be selected as desired to efficientlyidentify selected geometric shapes.

In certain embodiments of the invention, the above described scan linedefinition and inspection, and shape detection scheme can be performediteratively. For example, the scan lines can be individually defined andinspected, followed by probabilistic shape detection. In this way, oncethe shape is detected to within a user-defined probabilisticallysignificant level, only those scan lines necessary for such detectionmay be defined and inspected. Further, instead of defining andinspecting scan lines in an angularly-progressive pattern, scan linesmay be defined and inspected in a random or other pre-defined pattern(e.g., in a pattern similar to a torque pattern for lug nuts on anautomobile wheel). These and other variations to the shape detectionscheme are intended to be within the scope of certain embodiments of thepresent invention.

Certain detection algorithms for certain geometric shapes possess anadditional property that allows the detection algorithm to be executedfor any starting point in the geometric shape. The scan line methoddescribed above is one example of such an algorithm. Continuing with theexample presented above, for any starting point in the circle, the scanline scheme can detect the circle and deduce its properties includingdefining properties such as the center and the radius of the circle.Geometric shapes that can be efficiently detected by such algorithms orare otherwise susceptible to probabilistic detection methods willhereinafter be referred to as “any-point-detection-shapes.”

To find an any-point-detection-shape inside a captured image when thelocation of such any-point-detection-shapes is not known, a searchalgorithm can be used that randomly picks points in the image and checksif the point is inside the any-point-detection-shape. When theany-point-detection-shape in question occurs only once in an image, theany-point-detection-shape algorithm may terminate after finding theany-point-detection-shape. Furthermore, if multiple suchany-point-detection-shapes exist in the image, theany-point-detection-shape algorithm may terminate after detecting one ormore of the shapes. Typically, performance of such an algorithm isproportional to the size of the geometric shape. However, if the imagedoes not contain an any-point-detection-shape, the worst caseperformance of such an algorithm is that it will terminate indicatingfailure or it may not report success within a maximum allowed time.

Other algorithms can be used to find the location of anany-point-detection-shape in an image. In certain embodiments of theinvention, another example of an any-point-detection-shape detectionalgorithm is based on the assumption that the image contains the largestpossible any-point-detection-shape. Accordingly, a maximum limit forsize of the any-point-detection-shape can be calculated. Next, a set ofpossible starting points for the detection algorithm can be determinedor calculated using certain properties of the geometric shape andcertain characteristics of the detection algorithm. This set of startingpoints can be selected such that (a) selected locations increase theprobabilistic likelihood of detecting the any-point-detection-shape inthe image if such shape of the specified size or range of sizes exists,and (b) the number of locations in the set is selectively reduced tofacilitate detection of the any-point-detection-shape of maximum size.Typically, one or more of the reduced set of points is used as astarting point by the detection algorithm.

Starting points are typically selected such that a shape of a specifiedsize cannot be placed in the image such that none of the starting pointslie within the shape. The set of starting points can be selected toprovide a desired pattern of points having a desired separation betweenthe points. Thus, if a shape of the specified size exists, the detectionalgorithm would be able to detect the geometric shape, and the size ofthe detected shape can be compared with the estimate. If anany-point-detection-shape of a certain size in the specific locationscannot be found, then the estimated size of the geometric shape can bereduced, and the search can be extended to detect geometric shapes thatlie between the previously sought size and shapes of the reduced size.Based on the new constraint on the size of the geometric shape, a newset of possible points can be selected such that if a geometric shape ofa size between the specified constraints exists in the image, one of thepoints will be inside the geometric shape. The detection algorithm canthen be executed for the new set of starting points. Similarly, if ashape that satisfies the new size constraints is not be found, the sizeconstraint can be changed (i.e., reduced) again, a new set of startingpoints can be selected and the search progresses based on the new set ofstarting points.

FIGS. 11-14 depict progressively closer sets of starting points asdescribed in the preceding example. As illustrated, the sets of startingpoints include a gradually increasing number of points that correspondto a gradually reducing size estimate, or constraint. Again, in theexample, the any-point-detection-shape to be detected is assumed to be acircle. In the example of FIG. 11, twelve points are shown within theimage area 50 and the points are selected such that a circle having adiameter that is at least one third of the image width 52 can bedetected. A circle having a diameter of at least one third of the imagewidth 52 will encompass at least one of the twelve points. FIG. 12 showsa next set of points in the example. For this second iteration, a set ofstarting points is selected such that at least one of the startingpoints will be located within the bounds of a circle having a diameterthat is greater than or equal to about one sixth of the image width 52.FIG. 13 shows a next iteration in which a set of starting points isselected such that at least one of the starting points will be locatedwithin the bounds of a circle having a diameter that is greater than orequal to about one twelfth of the image width 52. FIG. 14 shows a nextiteration in which a set of starting points is selected such that atleast one of the starting points will be located within the bounds of acircle having a diameter that is greater than or equal to about onetwenty-fourth of the image width 52.

In certain embodiments, detection may be suspended after a desirednumber of iterations. However, detection may continue using one or morefiner resolution searches. In the example, the algorithm can terminateafter finding an any-point-detection-shape or can terminate with anindication that an any-point-detection-shape of a minimum specified sizedoes not exist. As should now be apparent to those skilled in the art,similar such algorithms can be defined for any otherany-point-detection-shape. Likewise, other starting point patterns canbe defined for the circle shape or for any otherany-point-detection-shape. These and other variations to this algorithmare intended to be within the scope of certain embodiments of thepresent invention.

Referring again to FIGS. 3-5, if the finder pattern 31 of a barcode orsymbol 30 is an any-point-detection-shape, determining the location ofthe barcode or symbol 30 typically includes finding the location of thefinder pattern 31 using one of the algorithms described above.Furthermore, if the barcode or symbol 30 occupies a large portion of theimage and if the size of the finder pattern 31 is larger than othershapes in the barcode, the location of the finder pattern 31 can beeasily found with few selected started points.

In certain embodiments of the invention, using anany-point-detection-shape as a finder pattern facilitates the detectionof the barcode, and thus the decoding of that barcode, by first findingthe possible positions of the finder pattern and then searching for theposition patterns around the finder pattern. This can be achieved bydesigning barcodes where certain properties of the barcode can berecovered from the size of the any-point-detection-shape, furtherrestricting certain properties of the position detection and alignmentpatterns. For example, if the finder pattern is a circle, the radius ofthe circle can restrict where the location patterns can be found. Onepossible example of such a restricted area is shown in FIG. 15. Asshown, the finder pattern is expected to be within the inner circle. Anyfinder pattern outside this expected area can be discarded, enablingthat same shape to be used for alignment and position patterns.

Furthermore, in certain embodiments of the invention, if different shapesizes are used for finder, position and alignment patterns, certainother properties of the shapes can be also restricted. For example, thesize of the data position pattern can be calculated by a formula thatuses the size of the finder pattern as a variable. In FIG. 15, the sizeof the data position pattern is approximately ⅓ of the size of thefinder pattern. The algorithm defined above used to detect the finderpattern will also avoid classifying smaller patterns as finder patterns,since the algorithm starts the search with the maximum possible size forthe finder pattern and then searches for smaller patterns only until thefinder pattern is found (assuming one exists at all). Even though thereis a possibility of detecting the position, alignment, or other similarpatterns (i.e., smaller patterns of the same expected shape) whilesearching for the finder pattern, the algorithm would reject those asthe size criteria would not be satisfied.

In certain embodiments of the invention, any-point-detection-shapes canbe used for other patterns in the barcode as well. For example, if anany-point-detection-shape is used for position detection patterns, thesize of the position pattern can be known to be a certain scale of thefinder pattern and the location of the position patterns can berestricted to an area in the image, a set of points can be selected suchthat at least one point falls inside each of the position detectionpatterns' restricted areas. FIG. 16 illustrates an example of an image,where the size of the position and alignment patterns can be derivedfrom other patterns within the image, generated according to certainembodiments of the present invention. This allows detecting the positiondetection patterns by running the detection algorithm on very fewpoints, facilitating an increased detection speed.

In certain embodiments of the invention, this same algorithm can be usedfor alignment patterns as well. For example, as shown in FIG. 17,perspective distortions may cause an incorrect estimate for the locationof the alignment pattern. However because the estimate for the centerfor the alignment pattern falls within the alignment pattern, it can becorrected through recalculation of the center of the alignment patternby running the any-point-detection-shape algorithm.

In certain embodiments of the invention, large alignment patterns 34 maybe used when significant deviation or distortion is expected. Asdiscussed above for larger finder pattern 31, larger alignment patterns34 can improve alignment reliability. Furthermore, small alignmentpatterns 34 can be used when smaller corrections are anticipated.Efficiency of the barcode or symbol 30 can be optimized by selectivelydispersing large and smaller alignment patterns 34. Improvements inefficiency can typically be obtained without a decrease of decodingreliability. For example, as shown in FIG. 17, a large perspectivedistortion can be corrected by recalculating the center of the alignmentpattern 341. FIG. 18 illustrates an example in which a smaller circlecan be used when the possible error in the estimate is lower.

In certain embodiments of the invention, the use of differently sizedand strategically located any-point-detection-shapes for finder 31,position detection patterns 32 and alignment patterns 34 can create atiming-patternless barcode or symbol 30. Typically, timing-patterns arealternating sequences of dark and light modules that enable modulecoordinates within the symbol to be determined. Timing patterns 110, 112in two popular barcode standards are shown in FIGS. 19-20. FIG. 19 showsthe timing patterns 110 in the QRCode barcode and FIG. 20 shows thetiming patterns 112 in DataMatrix barcode. In QRCode and DataMatrix, thehorizontal and vertical timing patterns respectively consist of a onemodule wide row and/or column of alternating dark and light modules,commencing and ending with a dark module. In QRCode, the horizontaltiming pattern runs across row 6 of the symbol between the separatorsfor the upper position detection patterns and the vertical timingpattern similarly runs down column 6 of the symbol between theseparators for the left-hand position detection patterns. The timingpatterns enable the symbol density and version to be determined andprovide datum positions for determining module coordinates.

A barcode that uses differently sized and strategically locatedany-point-detection-shapes for finder, position and alignment patterns,as in certain embodiments of the present invention, allows correction ofestimations for the modules 35 in a recursive fashion without usingtiming pattern. Typically, a barcode without a timing pattern can use alarge number of any-point-detection-shapes as alignment patterns 34where the modules 35 are dispersed between the alignment patterns 34.The location of the modules 35 can be easily calculated using simplegeometry. For example, consider the symbol 30 shown in FIG. 21, if thelocation of finder pattern 31 and alignment patterns 34 are known, thenthe location of the modules 35 can be determined by dividing imaginaryline segments joining neighboring alignment patterns 34 (or the finderpattern and neighboring alignment patterns) into 3. The one third (⅓)points of these line segments can be used as the location of the modules35, and the intensity and the color at those points can be then used todecide the value that the module 35 represents. Put another way, incertain embodiments, the centers of no more than two modules can existequidistantly between two neighboring alignment patterns 34 or betweenthe finder pattern 31 and a neighboring alignment pattern 34. In thisexample, neighboring patterns refers to two patterns that, if a linewere drawn between their centers, no other alignment pattern or finderpattern would be bisected by that line.

FIG. 22 illustrates an exemplary geometric calculation of how modulelocation can be derived according to certain embodiments of the presentinvention. As shown in FIG. 22, the distance between point A 220 andpoint C 222 are each located at the ⅔ position on a line connectingtheir respective neighboring alignment pattern with the finder pattern31, as measured from the finder pattern 31. Specifically, A 220 is 2L₁/3distant from the center of the finder pattern and L₁/3 distant from thecenter of the respective neighboring alignment pattern 34 (likewise forC 222 with respect to L₂). As this example illustrates, if the locationsof the finder pattern 31 and the alignment patterns 34 have been found,then the location of points A 220 and C222 can easily found. Then, thesepoints can be used as estimates for the center of modules, and the lightintensity and color at these points can be used to assign value for themodules. For example, upon processing, the intensity at point A 220would indicate a light color and the intensity at point C 222 willindicate a dark color.

Similarly in FIG. 22, the distance between point B 224 and point D 226are each located at a position beyond a line connecting their respectiveneighboring alignment pattern 34 with the finder pattern 31 at adistance of ⅓ of the length of the connecting line. In particular, B 224is L₁/3 distant and D 226 is L₂/3 distant from the center of theirrespective neighboring alignment patterns 34. The intensity and color atthese points can also be used as the value of the module.

Based on the symbology used, it is possible that certain modules may notlie between two patterns. For example, the point F 228 is such a module.If the number of such modules is low, it is possible to leave suchmodules unencoded without significantly reducing the density of thesymbology. It is also possible to use the estimated position ofsurrounding modules to estimate the location of such modules. Forexample, the location of the point F 228 can be calculated based on theestimated location of the point G 230 and H 232. If the modules arerepresented using any-point-detection-shapes, than the estimate for thelocation of such modules can be enhanced by re-estimating the center ofthe module using the any-point-detection-shape properties.

Although the present invention has been particularly described withreference to embodiments thereof, it should be readily apparent to thoseof ordinary skill in the art that various changes, modifications,substitutes and deletions are intended within the form and detailsthereof, without departing from the spirit and scope of the invention.Accordingly, it will be appreciated that in numerous instances somefeatures of the invention will be employed without a corresponding useof other features. Further, those skilled in the art will understandthat variations can be made in the number and arrangement of inventiveelements illustrated and described in the above figures. It is intendedthat the scope of the appended claims include such changes andmodifications. The scope of the present invention should, therefore, bedetermined not with reference to the certain embodiments presentedabove, but instead should be determined with reference to the pendingclaims along with their full scope of equivalents.

1. A method, comprising: detecting at least one any-point-detectionshape in an image of a symbol using a probabilistic detection, thesymbol having a plurality of modules collectively encoding informationand a finder pattern centered at a reference point in the symbol,wherein the at least one any-point-detection shape is distinguishable byshape from the plurality of modules and arranged into an alignmentpattern having a geometric relationship with the finder pattern and asubset of the plurality of modules, the detecting including: detectingboundary points of the alignment pattern on a set of angularly displacedscan lines that intersect at a starting point within the image; andidentifying the alignment pattern by correlating the boundary pointswith a selected geometrical shape of the at least oneany-point-detection shape.
 2. The method of claim 1, further comprisingdetecting a position pattern within the symbol.
 3. The method of claim2, further comprising distinguishing by shape the position pattern fromthe plurality of modules.
 4. The method of claim 2, further comprisingdistinguishing by color at least one of the finder pattern, thealignment pattern, or the position pattern from the plurality ofmodules.
 5. The method of claim 2, further comprising distinguishing bycolor the finder pattern from at least one of the alignment pattern orthe position pattern.
 6. The method of claim 2, further comprisingdistinguishing by size the finder pattern from the position pattern. 7.The method of claim 1, wherein the detecting the at least oneany-point-detection shape includes detecting the at least oneany-point-detection shape sized according to a formula that includes asize of the finder pattern as a variable.
 8. The method of claim 1,further comprising: detecting, as the plurality of modules, a pluralityof substantially square modules; and detecting, as the finder pattern, asubstantially circular finder pattern.
 9. The method of claim 1, whereinthe detecting the at least one any-point-detection shape comprisesdetecting at least one substantially circular any-point-detection shape.10. A system, comprising: a barcode reader configured to: scan a symbolcomprising a plurality of modules that encode information, a finderpattern located at a reference point and having a different geometricshape than the plurality of modules, and an alignment pattern in ageometric relationship with the finder pattern and a subset of theplurality of modules, wherein the alignment pattern comprises at leastone element having an any-point-detection shape that is different thanthe plurality of modules; detect boundary points of the alignmentpattern on a set of angularly displaced scan lines intersecting at astarting point within an image of the symbol; and identify the alignmentpattern by correlation of the boundary points with a known geometricalshape of the alignment pattern.
 11. The system of claim 10, wherein thebarcode reader is further configured to calculate a location of a centerof at least one of the plurality of modules relative to the at least oneelement having the any-point-detection shape.
 12. The system of claim10, wherein the barcode reader is further configured to detect aposition pattern within the symbol.
 13. The system of claim 10, whereinthe plurality of modules are substantially square, and theany-point-detection shape is substantially circular.
 14. The system ofclaim 10, wherein the barcode reader is further configured todistinguish by color at least one of the finder pattern, the alignmentpattern, or the position pattern from the plurality of modules.
 15. Acomputer-readable medium having stored thereon computer-executablecomponents that, in response to execution, cause a computing system toperform operations, including: reading an image of a symbol thatincludes at least one any-point-detection shape, a plurality of modulescollectively encoding information, and a finder pattern centered at areference point in the symbol, wherein the at least oneany-point-detection shape is distinguishable by shape from the pluralityof modules and arranged into an alignment pattern having a geometricrelationship with the finder pattern and a subset of the plurality ofmodules; and detecting the at least one any-point-detection shape usingprobabilistic detection, the detecting including: detecting boundarypoints of the alignment pattern on a set of angularly displaced scanlines that intersect at a starting point within the image; andidentifying the alignment pattern by correlating the boundary pointswith a selected geometrical shape of the at least oneany-point-detection shape.
 16. A system, comprising: means for inputtingan image of a symbol having at least one any-point-detection shape, aplurality of modules encoding information, and a finder pattern centeredat a reference point in the symbol, wherein the at least oneany-point-detection shape is distinguishable by shape from the pluralityof modules and arranged as an alignment pattern having a geometricrelationship with the finder pattern and a subset of the plurality ofmodules; and means for detecting the at least one any-point-detectionshape using probabilistic detection, the means for detecting including:means for detecting boundary points of the alignment pattern on a set ofangularly displaced scan lines intersecting at a starting point withinthe image; and means for correlating the boundary points with a selectedgeometrical shape of the at least one any-point-detection shape toidentify the alignment pattern.